Reclustering and finding Markers
SO4 <- SCTransform(SO4) %>%
RunPCA() %>%
FindNeighbors(dims = 1:30) %>%
FindClusters(resolution = 0.3) %>%
RunUMAP(dims = 1:30)
## Running SCTransform on assay: RNA
## Warning: The `slot` argument of `GetAssayData()` is deprecated as of SeuratObject 5.0.0.
## ℹ Please use the `layer` argument instead.
## ℹ The deprecated feature was likely used in the Seurat package.
## Please report the issue at <https://github.com/satijalab/seurat/issues>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## vst.flavor='v2' set. Using model with fixed slope and excluding poisson genes.
## Calculating cell attributes from input UMI matrix: log_umi
## Variance stabilizing transformation of count matrix of size 22376 by 11431
## Model formula is y ~ log_umi
## Get Negative Binomial regression parameters per gene
## Using 2000 genes, 5000 cells
## There are 2 estimated thetas smaller than 1e-07 - will be set to 1e-07
## Found 306 outliers - those will be ignored in fitting/regularization step
## Second step: Get residuals using fitted parameters for 22376 genes
## Computing corrected count matrix for 22376 genes
## Calculating gene attributes
## Wall clock passed: Time difference of 19.32625 secs
## Determine variable features
## Centering data matrix
## Place corrected count matrix in counts slot
## Warning: The `slot` argument of `SetAssayData()` is deprecated as of SeuratObject 5.0.0.
## ℹ Please use the `layer` argument instead.
## ℹ The deprecated feature was likely used in the Seurat package.
## Please report the issue at <https://github.com/satijalab/seurat/issues>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## Set default assay to SCT
## PC_ 1
## Positive: Umod, Egf, Tmem52b, Sult1d1, Sostdc1, Klk1, Prdx5, Fabp3, Cox6c, Wfdc2
## Foxq1, Ly6a, Wfdc15b, Slc25a5, Cox5b, Atp5g1, Krt7, Ckb, Cox4i1, Atp1a1
## Cldn19, Ndufa4, Cox8a, Ggt1, Atp5b, Chchd10, Cox6b1, Atp1b1, Uqcr11, Cox7a2
## Negative: Pappa2, Malat1, Gm37376, Zfand5, Mir6236, Robo2, Aard, Neat1, Wwc2, Pde10a
## Itga4, Nadk2, Col4a4, Nos1, Col4a3, Sgms2, S100g, Irx1, Ptgs2, Etnk1
## Ramp3, Sdc4, Srrm2, Itprid2, Hnrnpa2b1, Tmem158, Bmp3, Ranbp3l, Camk2d, Zbtb20
## PC_ 2
## Positive: Egf, Mir6236, Malat1, Gm37376, Umod, CT010467.1, Tmem52b, Nme7, Neat1, Slc12a1
## Gm24447, Kcnq1ot1, Atp1b1, Etnk1, Wnk1, Gm28438, Lars2, Sult1d1, Dst, Foxq1
## Slc5a3, Atrx, Pnisr, Syne2, Ivns1abp, Hnrnpa2b1, Gls, Srrm2, Gm23935, Col4a4
## Negative: Gm22133, Ubb, Fth1, Mt1, Ftl1, Hspa1a, Ldhb, H3f3b, Eif1, Car15
## Prdx1, Cd63, Hspa1b, Fos, Junb, Cd9, Mt2, Fxyd2, Mpc2, Mgst1
## Clu, Tmem213, Bsg, Jun, Jund, Actb, Spp1, Aldoa, Itm2b, Btg2
## PC_ 3
## Positive: Fos, Junb, Jun, Hspa1b, Btg2, Hspa1a, Egr1, Zfp36, Atf3, Ier2
## Fosb, Klf6, Socs3, Klf2, Jund, Dnajb1, Dusp1, Gadd45g, Rhob, Tsc22d1
## Gadd45b, Ubc, Malat1, Cebpd, H1f2, Klf4, Gm42793, H2bc4, H3f3b, Nr4a1
## Negative: Pappa2, Car15, Gm22133, Cd9, Mgst1, Ldhb, Fth1, Aard, Itm2b, Mpc2
## Cd63, Tmem59, Bsg, Prdx1, Clu, Tmem213, Ramp3, Tmem158, Ftl1, Tmbim6
## Mcub, Sfrp1, Epcam, Ndfip1, Fxyd2, Tmem176a, Irx1, Ly6a, Mdh1, Rabac1
## PC_ 4
## Positive: Pappa2, Aard, Umod, Tmem52b, Egf, Sult1d1, Tmem158, Mcub, Car15, Ptgs2
## Wwc2, Foxq1, Hsp90b1, Dctd, Iyd, Cd9, Ptprz1, Wnk1, Ramp3, Fth1
## Cdkn1c, Clu, Pth1r, Gm44120, Hspa5, Sec14l1, Ctsc, Calr, Col4a3, 1700028P14Rik
## Negative: Mt1, Gm8420, Apoe, Mt2, Gm8885, Gm28438, Aebp1, Gm24447, Sostdc1, Gpx6
## Gm28437, Neat1, Gm11808, Gm28037, Gm3511, Fgf9, Atp5md, Egfl6, Fxyd2, Gm45895
## Ivns1abp, Defb1, Gm11353, Atp5k, Gm12338, Gm10221, Cox6c, Car4, Gm6136, Ptger3
## PC_ 5
## Positive: S100g, Gm8420, Gm8885, Actb, Tmem52b, Gm11808, Gm11353, Ppia, Tmsb10, Gm6136
## Gm3511, Gm12338, Gm9616, Gm10029, Igfbp7, Gm7206, Gm9843, Gm28037, Gm10221, Serf2
## Atp5md, Gm12254, Atp5e, Aard, Ndufb1-ps, Cox6c, Gm4332, Cebpd, Pappa2, Gm5526
## Negative: Hspa1a, Klk1, Hspa1b, Gm22133, CT010467.1, Car15, Gm13340, Fth1, Ptger3, Itm2b
## Atp1a1, Cldn10, Hspa8, Apoe, Cd63, Aebp1, Ftl1, Mt2, Gpx6, Uba52-ps
## Wfdc2, Jun, Fau, Mt1, Pik3r1, Lpl, Gm6061, Clcnkb, Sostdc1, Gm3362
## Computing nearest neighbor graph
## Computing SNN
## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
##
## Number of nodes: 11431
## Number of edges: 377988
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.8604
## Number of communities: 6
## Elapsed time: 1 seconds
## Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
## To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
## This message will be shown once per session
## 13:24:41 UMAP embedding parameters a = 0.9922 b = 1.112
## 13:24:41 Read 11431 rows and found 30 numeric columns
## 13:24:41 Using Annoy for neighbor search, n_neighbors = 30
## 13:24:41 Building Annoy index with metric = cosine, n_trees = 50
## 0% 10 20 30 40 50 60 70 80 90 100%
## [----|----|----|----|----|----|----|----|----|----|
## **************************************************|
## 13:24:41 Writing NN index file to temp file /var/folders/7n/x74qctp91rng390gx0z9hmd80000gn/T//Rtmp79q1Om/file15bb56afd9027
## 13:24:41 Searching Annoy index using 1 thread, search_k = 3000
## 13:24:43 Annoy recall = 100%
## 13:24:43 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
## 13:24:44 Initializing from normalized Laplacian + noise (using RSpectra)
## 13:24:44 Commencing optimization for 200 epochs, with 506010 positive edges
## 13:24:44 Using rng type: pcg
## 13:24:46 Optimization finished
SO.markers <- FindAllMarkers(SO4, only.pos = TRUE)
## Calculating cluster 0
## Warning: `PackageCheck()` was deprecated in SeuratObject 5.0.0.
## ℹ Please use `rlang::check_installed()` instead.
## ℹ The deprecated feature was likely used in the Seurat package.
## Please report the issue at <https://github.com/satijalab/seurat/issues>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## For a (much!) faster implementation of the Wilcoxon Rank Sum Test,
## (default method for FindMarkers) please install the presto package
## --------------------------------------------
## install.packages('devtools')
## devtools::install_github('immunogenomics/presto')
## --------------------------------------------
## After installation of presto, Seurat will automatically use the more
## efficient implementation (no further action necessary).
## This message will be shown once per session
## Calculating cluster 1
## Calculating cluster 2
## Calculating cluster 3
## Calculating cluster 4
## Calculating cluster 5
SO.markers %>%
group_by(cluster) %>%
dplyr::filter(avg_log2FC > 1)
## # A tibble: 782 × 7
## # Groups: cluster [6]
## p_val avg_log2FC pct.1 pct.2 p_val_adj cluster gene
## <dbl> <dbl> <dbl> <dbl> <dbl> <fct> <chr>
## 1 3.11e-176 1.09 0.628 0.384 6.97e-172 0 Mcub
## 2 5.73e-125 1.47 0.503 0.302 1.28e-120 0 Aard
## 3 1.75e- 68 1.15 0.226 0.105 3.92e- 64 0 Fetub
## 4 3.70e- 63 1.51 0.141 0.051 8.27e- 59 0 Begain
## 5 3.58e- 51 1.19 0.162 0.072 8.01e- 47 0 Crybb1
## 6 1.56e- 41 1.01 0.269 0.167 3.49e- 37 0 Iyd
## 7 1.71e- 34 1.04 0.126 0.06 3.83e- 30 0 Lmo2
## 8 3.28e- 27 1.15 0.097 0.045 7.34e- 23 0 Gm8257
## 9 1.90e- 25 1.35 0.066 0.026 4.25e- 21 0 Mest
## 10 1.23e- 22 1.32 0.065 0.027 2.76e- 18 0 Hmox1
## # ℹ 772 more rows
SO.markers %>%
group_by(cluster) %>%
dplyr::filter(avg_log2FC > 1) %>%
slice_head(n = 10) %>%
ungroup() -> top10
DoHeatmap(SO4, features = top10$gene) + NoLegend()
## Warning in DoHeatmap(SO4, features = top10$gene): The following features were
## omitted as they were not found in the scale.data slot for the SCT assay: Phkg1,
## Gm26982, Rtp4, Rsad2, Oasl2, Oasl1, Il3ra, Gm3500, Gm8257
DimPlot(SO4,)
DimPlot(SO4, split.by = "sample")
markers.to.plot2 <- c("Lrp2", # PT
"Slc5a12", # PT-S1
"Slc13a3", # PT-S2
"Slc16a9", # PT-S3
"Havcr1", # Injured PT
"Epha7", # dTL
"Cryab", # dTL
"Cdh13", # dTL1
"Slc14a2", # dTL2
"Slc12a1", # TAL
"Umod", # TAL, DCT1
"Egf", # TAL, DCT1,
"Cldn10", # TAL
"Cldn16", # TAL
"Nos1", # MD
"Slc12a3", # DCT
"Pvalb", # DCT1
"Slc8a1", # DCT2, CNT
"Aqp2", # PC
"Slc4a1", # IC-A
"Slc26a4", # IC-B
"Nphs1", # Podo
"Ncam1", # PEC
"Flt1", # Endo
"Emcn", # Glom Endo
"Kdr", # Capillary Endo
"Pdgfrb", # Perivascular
"Pdgfra", # Fib
"Piezo2", # Mesangial
"Acta2", # Mural
"Ptprc", # Immune
"Cd74", # Macrophage
"Skap1", # B/T Cells
"Upk1b", # Uro
"Top2a", # Proliferation
"Cldn5",
"Jun",
"Fosb"
)
DotPlot(SO4,
features = markers.to.plot2,
dot.scale = 8,
dot.min = 0,
scale.max = 100,
scale.min = 0,
col.min = -2.5,
col.max = 2.5)+
coord_flip()
## Warning: The following requested variables were not found: Slc5a12, Slc13a3,
## Havcr1, Kdr, Pdgfrb, Pdgfra, Piezo2, Upk1b
markers.to.plot1 <- c("Ramp3", # PT
"Egf", # PT-S1
"Fos",
"Cxcl10") # PT-S3
DotPlot(SO4,
features = markers.to.plot1,
dot.scale = 8,
dot.min = 0,
scale.max = 100,
scale.min = 0,
col.min = -2.5,
col.max = 2.5)+
coord_flip()
DimPlot(SO4)
DimPlot(SO4,group.by = "treatment",split.by = "treatment")
DimPlot(SO4,split.by = "sample")
